2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)最新文献

Snake-Stega: A snake game-based steganography scheme snake - stega:基于蛇类游戏的隐写机制

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10177024

Susmita Mahato

引用次数: 0

Dependency analysis of various factors and ML models related to Fertilizer Recommendation 肥料推荐相关因素及ML模型的相关性分析

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176974

S. Vaishnavi, Nandikaa Shanmugam, Galla Kiran, A. Priyadharshini

{"title":"Dependency analysis of various factors and ML models related to Fertilizer Recommendation","authors":"S. Vaishnavi, Nandikaa Shanmugam, Galla Kiran, A. Priyadharshini","doi":"10.1109/ICSCCC58608.2023.10176974","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176974","url":null,"abstract":"Adopting the same fertilizer gives minimum yield to the farmers as soil properties have changed drastically due to the change in environmental condition. In literature, different algorithmic analysis has been carried out to predict the fertilizer considering various factors, however, there is a gap in identifying every possible factor relevant to fertilizer recommendation. Hence, in our proposed work, we have utilized various soil and environmental factors like Nitrogen, Phosphorus and Potassium values, humidity, rainfall, weather condition and performed a dependency analysis of these factors to give a more accurate fertilizer prediction so as to enhance the crop yield. Algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) have been explored to study the suitability of these algorithms in fertilizer prediction. The presented algorithms are compared based on the performance metrics such as accuracy, F1 score, Recall and precision. It is found that, among other algorithms, SVM performed better with maximum accuracy of 97% when all the factors are taken into account.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images 模糊对比度增强与近不可见图像分割

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176516

Zaheeruddin Syed, Kanneboina Siddhartha, Thota Rahul, Aragonda Sneha, Ellandala Jhansi, K. Suganthi

{"title":"Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images","authors":"Zaheeruddin Syed, Kanneboina Siddhartha, Thota Rahul, Aragonda Sneha, Ellandala Jhansi, K. Suganthi","doi":"10.1109/ICSCCC58608.2023.10176516","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176516","url":null,"abstract":"Any computer vision application must first improve a picture before continuing to process it color details losses during the enhancement process is a prevalent issue with most current techniques when applied to photographs that are essentially unnoticeable the qualitatively undetectable image should be improved while maintaining its freshness and coloring. Histogram equalization, a traditional approach of contrast enhancement, resulting in more than enhancement of something like the picture, particularly one with poorer resolution. The objective of this research is to develop an innovative fuzzy inference system capable of enhancing the contrast of low-resolution photos while simultaneously addressing any existing limitations, existing techniques and segmenting the tumor in MRI images. The outcomes from the two methods are contrasted. Throughout this research, the technique results in a very tiny change in intensity value while maintaining the image's information about color and brightness. The method enhances striking contrast while preserving naturalness without introducing any artefacts. Active contour processing on these photos produces extremely accurate segmentation results. Mainly this is used to detect the tumor in MRI images with some basic morphological operations.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilevel Face Mask Detection System using Ensemble based Convolution Neural Network 基于集成卷积神经网络的多层人脸检测系统

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176502

V. K. Gupta, Avdhesh Gupta, Vikas Tyagi, Priyank Pandey, Richa Gupta, D. Kumar

{"title":"Multilevel Face Mask Detection System using Ensemble based Convolution Neural Network","authors":"V. K. Gupta, Avdhesh Gupta, Vikas Tyagi, Priyank Pandey, Richa Gupta, D. Kumar","doi":"10.1109/ICSCCC58608.2023.10176502","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176502","url":null,"abstract":"Due to COVID outbreak face mask detection in the industry as well as in any gathering playing an important role. Either person worn facemask or not worn. In this CORONA situation, Use of a face mask is one such preventative that is crucial. Facial recognition technologies are now used by many businesses and organizations for their own general purposes. We are all aware of how important it has become to always wear a mask when we travel. However, as we all know, it is impossible to monitor who is wearing a mask and who is not. If some person who worn the mask, then it is not confirmed whether he/she worn it correctly or not. We make the use of AI in our daily life. We achieve this with the help of a deep learning, where we train the model using various convolution neural network approaches and created a hybrid model using bagging-based ensemble learning. Here, detection is performed based on voting-based classification so that we can enhance the accuracy of our model. We have found dataset from MAFA and Kaggle. The hybrid approach of C2N model achieved exceptional accuracy with the use of a dataset of face mask detection that contains both with and without face mask photographs. In our multilevel facemask detection system at the first level our model will predict whether the person worn facemask or not and at its second level it will predict the correctness of facemask, whether it is worn correct or not.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129583817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber Attack Simulation and Detection in Digital Substation 数字化变电站网络攻击仿真与检测

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176955

Doney Abraham, Sule YAYILGAN YILDIRIM, Filip Holík, S. Acevedo, Alemayehu Gebremedhin

引用次数: 0

Probabilistic Data Structure in smart agriculture 智能农业中的概率数据结构

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176985

Gourav Singhal, Amritpal Singh

{"title":"Probabilistic Data Structure in smart agriculture","authors":"Gourav Singhal, Amritpal Singh","doi":"10.1109/ICSCCC58608.2023.10176985","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176985","url":null,"abstract":"In the modern world, the use of IoT devices and emerging technologies are contributing to a daily escalation in data generation. Numerous novel approaches are arising to handle such copious amounts of data. The utilization of this data in making decisions related to agriculture, combined with the integration of smart agriculture techniques, can enhance the conventional agricultural system. Smart agriculture relies heavily on the seamless integration and coordination of various devices. Data retrieval, storage, and analysis are some of the crucial tasks in this field. Data security, privacy, real-time decision-making, and semi-structured and unstructured data are some of the challenges and limitations of using traditional approaches when dealing with a high amount of generated data. For handling data and getting a real-time response in smart agriculture Probabilistic Data Structures (PDS) are used as an effective and efficient solution for various applications. Providing a thorough analysis of how PDS applications are utilized in the realm of smart agriculture is the main objective of this paper. This study takes an in-depth look into the important area of smart agriculture, examining its inception, obstacles, areas of research that require further exploration, and possible future paths. This paper aims to provide a comprehensive examination of PDS in smart agriculture, catering to readers and researchers who seek to expand their knowledge in this area. Additionally, this paper aims to identify potential research opportunities within this field.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129270235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convolution Neural Network for Facial Kinship Verification 基于卷积神经网络的面部亲属关系验证

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176509

Kusum, Vijay Kumar

{"title":"Convolution Neural Network for Facial Kinship Verification","authors":"Kusum, Vijay Kumar","doi":"10.1109/ICSCCC58608.2023.10176509","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176509","url":null,"abstract":"The process of detecting whether two people in a given pair of face pictures are biologically related or not is known as facial kinship verification. Deep learning-based techniques, in particular Convolutional Neural Networks (CNNs), have excelled at this challenge in recent years. In this article, we will compare a number of pre-trained CNN models and hybrid models to one another. Also, try to check performance by ensembling these models. The models are trained on the large-scale KinFaceW-I dataset and evaluated on the KinFaceW- II dataset, achieving state-of-the-art performance. In order to evaluate the performance, we have made our new dataset similar to the KinFaceW dataset. Additionally, our technique exhibits resilience to a variety of facial variables, including alterations in age, posture, and expression. Overall, can get a potential answer to the problem of facial kinship verification, which is crucial in numerous disciplines such as forensic investigation, family history research, and social media analysis. At last paper identified a ensembled or single models which work well on KinFace dataset and new dataset introduced by this paper.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124108514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization 基于迁移学习CNN和超参数优化的x线胸片肺炎病例深度学习检测

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176853

S. Agrawal, Pragati Agrawal

{"title":"Detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization","authors":"S. Agrawal, Pragati Agrawal","doi":"10.1109/ICSCCC58608.2023.10176853","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176853","url":null,"abstract":"Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128078227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Precision Agriculture: Crop Image Segmentation and Loss Evaluation through Drone Surveillance 精准农业:无人机监控作物图像分割与损失评估

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176980

P. Patidar, D. Tomar, R. Pateriya, Y. K. Sharma

引用次数: 0

Intrusion Detection Techniques For Security and Privacy of 6G Applications 6G应用的安全与隐私入侵检测技术

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2023-05-26 DOI: 10.1109/ICSCCC58608.2023.10176740

Priya Kohli, Sachin Sharma, Priya Matta

引用次数: 4